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Conjugation of Hypersoft Set and Triangular Neutrosophic to Evaluate Cognitive Digital Twins' Contribution to Smart Manufacturing

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Neutrosophic Sets and Systems 2025年 80卷 632-644页

作者： Mohamed, Mona Alaa, Nurhan Higher Technological Institute 10th of Ramadan City44629 Egypt Department of Computer Science Faculty of Computers and Informatics Zagazig University Zagazig44519 Egypt

The latest innovation in digital twin technology is called Cognitive Digital Twins (CDT). The sophisticated and autonomous activities made possible by this technology have the potential to revolutionize manufacturing. An overview of CDT in manufacturing is given in this paper, along with an examination of their main features, components, and possible uses. CDT can learn from data, reason about difficult circumstances, and make well-informed judgments by combining artificial intelligence, machine learning, and knowledge representation approaches. The advantages of CDT in smart manufacturing are covered in the paper. Hence, the objective of this paper is evaluating the smart manufacturing that adopts CDT in its operations and practices to be smart. Multi-Criteria Decision Making (MCDM) as CRiteria Importance Through Intercriteria Correlation and multi-objective optimization based on simple ratio analysis (MOOSRA) are leveraged to construct soft decision models. This model can trat with vague and incomplete information through harnessing uncertainty theory especially Triangular Neurosophic Number (TriNN). Also, Hypersoft set is utilized with MOOSRA to rank alternatives of smart manufacturing. © 2025, University of New Mexico. All rights reserved.

关键词： Smart manufacturing

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Contemporary Study for Detection of COVID-19 Using Machine Learning with Explainable AI

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computers, Materials & Continua 2024年第7期80卷 1075-1104页

作者： Saad Akbar Humera Azam Sulaiman Sulmi Almutairi Omar Alqahtani Habib Shah Aliya Aleryani Department of Computer Science University of KarachiKarachi75270Pakistan Department of Computing Faculty of Engineering Science and TechnologyHamdard UniversityKarachi75540Pakistan Department of Health Informatics College of Applied Medical SciencesQassim UniversityQassim51452Saudi Arabia Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia

The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic *** this article,a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19,pneumonia,and normal conditions in chest X-ray images(CXIs)is proposed coupled with Explainable Artificial Intelligence(XAI).Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3,VGG16,and VGG19 that excel in the task of feature *** methodology is further enhanced by the inclusion of the t-SNE(t-Distributed Stochastic Neighbor Embedding)technique for visualizing the extracted image features and Contrast Limited Adaptive Histogram Equalization(CLAHE)to improve images before extraction of ***,an AttentionMechanism is utilized,which helps clarify how the modelmakes decisions,which builds trust in artificial intelligence(AI)*** evaluate the effectiveness of the proposed approach,both benchmark datasets and a private dataset obtained with permissions from Jinnah PostgraduateMedical Center(JPMC)in Karachi,Pakistan,are *** 12 experiments,VGG19 showcased remarkable performance in the hybrid dataset approach,achieving 100%accuracy in COVID-19 *** classification and 97%in distinguishing normal ***,across all classes,the approach achieved 98%accuracy,demonstrating its efficiency in detecting COVID-19 and differentiating it fromother chest disorders(Pneumonia and healthy)while also providing insights into the decision-making process of the models.

关键词： COVID-19 detection deep neural networks support vector machine CXIs InceptionV3 VGG16 VGG19 t-SNE embedding CLAHE attention mechanism XAI

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Parametric test based on the bootstrapping approach for the MANOVA under a Behrens-Fisher problem

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Model Assisted Statistics and Applications 2024年第1期19卷 61-69页

作者： Singthongchai, Jatsada Thongmual, Noppakun Nitisuk, Nirun Department of Informatics and Computer Science Faculty of Sciences and Health Technology Kalasin University Nuea Thailand Department of Science and Mathematics Faculty of Sciences Kalasin University Nuea Thailand

This article presents a comparison of multivariate normal mean vectors under covariance positive definite matrices. We introduce an improved parametric bootstrap (IPB) approach for addressing the multivariate Behrens-Fisher problem, specifically focusing on cases with unequal covariance matrices. Additionally, we evaluate the performance of the IPB test by comparing it with three existing tests: the parametric bootstrap (PB) test, the generalized variable (GV) test, and the Johansen test. Through Monte Carlo simulation, our results demonstrate that both the IPB test and the PB test exhibit superior control over Type I error rates compared to the GV and Johansen tests. Notably, the IPB test outperforms the PB test in terms of controlling Type I error rates. Consequently, our study concludes that the IPB test represents a preferred statistical method for testing the equality of mean vectors in the multivariate Behrens-Fisher problem. © 2024 – IOS Press. All rights reserved.

关键词： Behrens-Fisher problem Parametric bootstrap Type I error rates unequal covariance

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A Comparative Study of Automatic Hate Speech Detection Using Machine Learning 1

A Comparative Study of Automatic Hate Speech Detection Using...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Siddiqui, Jawaid Ahmed Yuhaniz, Siti Sophiayati Memon, Zulfiqar Ali Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia Computer Science Department Karachi Pakistan

ISBN: (纸本)9798350373042

There is no denying that social media's ubiquitous use and the knowledge it seamlessly disseminates have improved humanity. But despite its many benefits, this growth has also given rise to urgent worries, including the spread of hate speech. Modern research have embraced a variety of feature engineering techniques and machine learning algorithms as a remedy to this increasing difficulty inside the world of social media platforms. These initiatives aim to automatically identify hate speech across several datasets, offering a promising way to lessen this pervasive problem. As far as we are aware that no research has directly compared different feature engineering methods with different machine learning algorithms to determine which method produces the best results on a representative public dataset. This article's goal is to evaluate how well three different feature engineering approaches work with eight different machine learning algorithms using an open source free publicly available datasets that include 03 different classes. The results of the experiment revealed that the combination of bigram features and the (SVM) support vector machine algorithm performed the best overall, with an amazing accuracy rate of '79%'. Our research consequences touch on actual situations, making it a landmark study that potentially laid the foundation for future efforts aimed at automatically identifying hate speech. Further, the results of these comparisons will serve as state-of-the-art approaches against which further studies of automated text classification can be measured. © 2024 IEEE.

关键词： Social networking (online)

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Decoding Digital Hostility: Examining and Addressing Hate Speech on Social Media Platforms 1

Decoding Digital Hostility: Examining and Addressing Hate Sp...

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1st IEEE Karachi Section Humanitarian Technology Conference, Khi-HTC 2024

作者： Siddiqui, Jawaid Ahmed Yuhaniz, Siti Sophiayati Memon, Zulfiqar Ali Razak Faculty of Technology and Informatics Kuala Lumpur Malaysia Computer Science Department Karachi Pakistan

ISBN: (纸本)9798350373042

There is no denying that the widespread usage of social media and the sharing of information have greatly benefited humanity. However, a number of issues have also emerged as a result of this increase in online engagement, most notably the spread of hate speech. Recent research has addressed this growing problem on social media platforms by automatically detecting hate speech in a variety of datasets using a variety of feature engineering approaches and machine/deep learning algorithms. It is interesting, though, that many of these studies - to the best of our knowledge - resort to identifying hate speech messages using traditional feature engineering techniques, which leads to less-than-ideal classification results. This is explained by the shortcomings of the feature engineering techniques now in use, specifically their vulnerability to the word order and word context problems. Therefore, more advanced strategies are desperately needed to address these issues and improve the precision of hate speech identification on social media platforms. To create fundamental lexical benchmarks is the goal. As distinguishing characteristics, our methodology makes use of the power of n-grams, which cover both character and word levels, and skip-grams, which cover both character and word levels. Notably, we successfully identify posts in all three defined categories with an impressive 78% accuracy. The results highlight how difficult it is to discriminate between vulgarities and hate speech. We also conduct a thorough exploration of potential directions for future research projects. © 2024 IEEE.

关键词： Machine learning

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Predictive Analysis of Sarajevo's AQI using Machine Learning Models for Varied Data Granularity and Prediction Windows 47

Predictive Analysis of Sarajevo's AQI using Machine Learning...

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47th ICT and Electronics Convention, MIPRO 2024

作者： Zolota, Emina Hasić, Vahidin Mević, Amina Delić, Amra Krivić, Senka University Of Sarajevo Faculty Of Electrical Engineering Department For Computer Science And Informatics Sarajevo Bosnia and Herzegovina

ISBN: (纸本)9798350382495

This study scrutinizes five years of Sarajevo's Air Quality Index (AQI) data using diverse machine learning models - Fourier autoregressive integrated moving average (Fourier ARIMA), Prophet, and Long short-term memory (LSTM) - to forecast AQI levels. Focusing on various prediction frames, we evaluate model performances and identify optimal strategies for different temporal granularities. Our research unveils subtle insights into each model's efficacy, shedding light on their strengths and limitations in predicting AQI across varied timeframes. This research presents a robust framework for automatic optimization of AQI predictions, emphasizing the influence of temporal granularity on prediction accuracy, automatically selecting the most efficient models and parameters. These insights hold significant implications for data-driven decision-making in urban air quality control, paving the way for proactive and targeted interventions to improve air quality in Sarajevo and similar urban environments. © 2024 IEEE.

关键词： Air quality

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Developing phoneme-based lip-reading sentences system for silent speech recognition

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CAAI Transactions on Intelligence Technology 2023年第1期8卷 129-138页

作者： Randa El-Bialy Daqing Chen Souheil Fenghour Walid Hussein Perry Xiao Omar HKaram Bo Li School of Engineering London South Bank UniversityLondonUK Faculty of Informatics and Computer Science British University in EgyptCairoEgypt School of Electronics and Informatics Northwestern Polytechnical UniversityXi'anChina

Lip-reading is a process of interpreting speech by visually analysing lip *** research in this area has shifted from simple word recognition to lip-reading sentences in the *** paper attempts to use phonemes as a classification schema for lip-reading sentences to explore an alternative schema and to enhance system *** classification schemas have been investigated,including characterbased and visemes-based *** visual front-end model of the system consists of a Spatial-Temporal(3D)convolution followed by a 2D *** utilise multi-headed attention for phoneme recognition *** the language model,a Recurrent Neural Network is *** performance of the proposed system has been testified with the BBC Lip Reading Sentences 2(LRS2)benchmark *** with the state-of-the-art approaches in lip-reading sentences,the proposed system has demonstrated an improved performance by a 10%lower word error rate on average under varying illumination ratios.

关键词： deep learning deep neural networks lip-reading phoneme-based lip-reading spatial-temporal convolution,transformers

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Fine-Tuning GPT-4o-Mini for Programming Questions Generation

Fine-Tuning GPT-4o-Mini for Programming Questions Generation

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2024 International Conference on computer and Applications, ICCA 2024

作者： Hesham, Alaa Hamdy, Abeer The British University in Egypt Faculty of Informatics and Computer Science Department of Software Engineering Cairo Egypt

ISBN: (纸本)9798350367560

Creating programming questions that are both meaningful and educationally relevant is a critical task in computer science education. This paper introduces a fine-tuned GPT4o-mini model (C2Q). It is designed to generate meaningful questions by leveraging semantic feature extraction and well- crafted prompts. The approach addresses the limitations of traditional generative models, offering a deeper understanding of programming code and producing questions that are precise, diverse, and relevant to a given code snippets. The proposed framework incorporates essential code elements, such as control structures and method attributes, to generate questions that align with programming concepts. Evaluation metrics used were BLEU, ROUGE-1, and ROUGE-L to evaluate the model's performance. The findings reveal that the model achieves better structural coherence and conceptual relevance while focusing on contextual understanding over exact term matching. This work highlights the potential of the proposed approach to advance teaching and assessment methods in computer science. © 2024 IEEE.

关键词： Program processors

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Sentiment Analysis Using K-NN Algorithm Through Random Search Cross Validation Approach 4

Sentiment Analysis Using K-NN Algorithm Through Random Searc...

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4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Senda, Tito Afwi Sitompul, Opim Salim Amalia, Amalia Universitas Sumatera Utara Master of Informatics Faculty of Computer Science and Information Technology Medan Indonesia

ISBN: (纸本)9798350376111

This research discusses the application of the KNearest Neighbor (K-NN) algorithm in sentiment analysis, specifically in classifying positive, negative, or neutral sentiments from ChatGPT user opinions collected from user reviews on the Play Store. This research focuses on optimizing the key parameters of the K-NN algorithm using the Random Search cross-validation (RSCV) method. The evaluation results showed a good performance improvement after applying RSCV. Previously, the model showed unsatisfactory performance but improved significantly after optimization. Overall, RSCV successfully improved the average performance of the model for sentiment analysis. © 2024 IEEE.

关键词： Natural language processing systems

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The Influence of ChatGPT on Student Learning and Academic Performance 5

The Influence of ChatGPT on Student Learning and Academic Pe...

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5th International Conference on computer and Applications, ICCA 2023

作者： Ayman, Shehab Eldeen El-Seoud, Samir A. Nagaty, Khaled Karam, Omar H. The British University In Egypt Faculty of Informatics and Computer Science Cairo Egypt

ISBN: (纸本)9798350303254

This study delves into the integration of ChatGPT, an artificial intelligence-driven language model, within undergraduate education. The research scrutinizes the potential advantages, obstacles, and ethical dimensions linked to including ChatGPT in educational practices. It assesses how ChatGPT may impact student engagement, critical thinking, problem-solving abilities, writing proficiency, and the delivery of personalized learning experiences. A survey administered to university faculty members captures their perspectives on ChatGPT usage, encompassing its influence on learning outcomes and academic performance. An internal inquiry at the British University in Egypt found high plagiarism rates in various faculties, with mass media having the highest at 66%, while political science had the lowest at 28%. A survey has been conducted on teaching staff at the university. 75% of teaching staff believes that ChatGPT should be integrated into teaching. However, they are still undecided about whether ChatGPT has affected the teaching and learning process. The study underscores the significance of responsible implementation, faculty training, and continuous assessment to optimize ChatGPT's benefits while adhering to ethical standards in education. In conclusion, this paper illuminates ChatGPT's potential as a valuable tool in undergraduate education, underscoring the imperative of preserving critical thinking skills and human interaction in the learning journey. © 2023 IEEE.

关键词： Students

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